Thresholding of cryo-EM density maps by false discovery rate control

Automated model-free analysis of cryo-EM volume ensembles with SIREn

Cryogenic electron microscopy (cryo-EM) has the potential to capture snapshots of proteins in motion and generate hypotheses linking conformational states to biological function. This potential has been increasingly realized by the advent of machine learning models that allow 100s-1,000s of 3D density maps to be generated from a single dataset. How to identify distinct structural states within these volume ensembles and quantify their relative occupancies remain open questions. Here, we present an approach to inferring variable regions directly from a volume ensemble based on the statistical co-occupancy of voxels, as well as a 3D convolutional neural network that predicts binarization thresholds for volumes in an unbiased and automated manner. We show that these tools recapitulate known heterogeneity in a variety of simulated and real cryo-EM datasets and highlight how integrating these tools with existing data processing pipelines enables improved particle curation.

In-cell Structure and Variability of Pyrenoid Rubisco

Ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) is a key enzyme in the global carbon cycle, catalyzing CO2 fixation during photosynthesis. To overcome Rubisco's inherent catalytic inefficiency, many photosynthetic organisms have evolved CO2-concentrating mechanisms. Central to these mechanisms is the pyrenoid, a protein-dense organelle within the chloroplast of eukaryotic algae, which increases the local concentration of CO2 around Rubisco and thereby enhances its catalytic efficiency. Although the structure of Rubisco has been extensively studied by in vitro methods such as X-ray crystallography and single particle cryo-EM, its native structure within the pyrenoid, its dynamics, and its association with binding partners remain elusive. Here, we investigate the structure of native pyrenoid Rubisco inside the green alga Chlamydomonas reinhardtii by applying cryo-electron tomography (cryo-ET) on cryo-focused ion beam (cryo-FIB) milled cells, followed by subtomogram averaging and 3D classification. Reconstruction at sub-nanometer resolution allowed accurate modeling and determination of a closed (activated) Rubisco conformation. Comparison to other reconstructed subsets revealed local variations at the complex active site and at the large subunit dimers interface, as well as association with binding proteins. The different structural subsets distribute stochastically within the pyrenoid. Taken together, these findings offer a comprehensive description of the structure, dynamics, and functional organization of Rubisco within the pyrenoid, providing valuable insights into its critical role in CO2 fixation.

Ligand identification in CryoEM and X-ray maps using deep learning

MOTIVATION

Accurately identifying ligands plays a crucial role in the process of structure-guided drug design. Based on density maps from X-ray diffraction or cryogenic-sample electron microscopy (cryoEM), scientists verify whether small-molecule ligands bind to active sites of interest. However, the interpretation of density maps is challenging, and cognitive bias can sometimes mislead investigators into modeling fictitious compounds. Ligand identification can be aided by automatic methods, but existing approaches are available only for X-ray diffraction and are based on iterative fitting or feature-engineered machine learning rather than end-to-end deep learning.

RESULTS

Here, we propose to identify ligands using a deep-learning approach that treats density maps as 3D point clouds. We show that the proposed model is on par with existing machine learning methods for X-ray crystallography while also being applicable to cryoEM density maps. Our study demonstrates that electron density map fragments can aid the training of models that can later be applied to cryoEM structures but also highlights challenges associated with the standardization of electron microscopy maps and the quality assessment of cryoEM ligands.

AVAILABILITY AND IMPLEMENTATION

Code and model weights are available on GitHub at https://github.com/jkarolczak/ligands-classification. An accompanying ChimeraX bundle is available at https://github.com/wtaisner/chimerax-ligand-recognizer.

Ligand Identification in CryoEM and X-ray Maps Using Deep Learning

Motivation

Accurately identifying ligands plays a crucial role in the process of structure-guided drug design. Based on density maps from X-ray diffraction or cryogenic-sample electron microscopy (cryoEM), scientists verify whether small-molecule ligands bind to active sites of interest. However, the interpretation of density maps is challenging, and cognitive bias can sometimes mislead investigators into modeling fictitious compounds. Ligand identification can be aided by automatic methods, but existing approaches are available only for X-ray diffraction and are based on iterative fitting or feature-engineered machine learning rather than end-to-end deep learning.

Results

Here, we propose to identify ligands using a deep learning approach that treats density maps as 3D point clouds. We show that the proposed model is on par with existing machine learning methods for X-ray crystallography while also being applicable to cryoEM density maps. Our study demonstrates that electron density map fragments can aid the training of models that can later be applied to cryoEM structures but also highlights challenges associated with the standardization of electron microscopy maps and the quality assessment of cryoEM ligands.

Availability

Code and model weights are available on GitHub at https://github.com/jkarolczak/ligands-classification. Datasets used for training and testing are hosted at Zenodo: 10.5281/zenodo.10908325. An accompanying ChimeraX bundle is available at https://github.com/wtaisner/chimerax-ligand-recognizer.

Roodmus: a toolkit for benchmarking heterogeneous electron cryo-microscopy reconstructions

Conformational heterogeneity of biological macromolecules is a challenge in single-particle averaging (SPA). Current standard practice is to employ classification and filtering methods that may allow a discrete number of conformational states to be reconstructed. However, the conformation space accessible to these molecules is continuous and, therefore, explored incompletely by a small number of discrete classes. Recently developed heterogeneous reconstruction algorithms (HRAs) to analyse continuous heterogeneity rely on machine-learning methods that employ low-dimensional latent space representations. The non-linear nature of many of these methods poses a challenge to their validation and interpretation and to identifying functionally relevant conformational trajectories. These methods would benefit from in-depth benchmarking using high-quality synthetic data and concomitant ground truth information. We present a framework for the simulation and subsequent analysis with respect to the ground truth of cryo-EM micrographs containing particles whose conformational heterogeneity is sourced from molecular dynamics simulations. These synthetic data can be processed as if they were experimental data, allowing aspects of standard SPA workflows as well as heterogeneous reconstruction methods to be compared with known ground truth using available utilities. The simulation and analysis of several such datasets are demonstrated and an initial investigation into HRAs is presented.

Automated model-free analysis of cryo-EM volume ensembles with SIREn

Cryogenic electron microscopy (cryo-EM) has the potential to capture snapshots of proteins in motion and generate hypotheses linking conformational states to biological function. This potential has been increasingly realized by the advent of machine learning models that allow 100s-1,000s of 3D density maps to be generated from a single dataset. How to identify distinct structural states within these volume ensembles and quantify their relative occupancies remain open questions. Here, we present an approach to inferring variable regions directly from a volume ensemble based on the statistical co-occupancy of voxels, as well as a 3D-convolutional neural network that predicts binarization thresholds for volumes in an unbiased and automated manner. We show that these tools recapitulate known heterogeneity in a variety of simulated and real cryo-EM datasets, and highlight how integrating these tools with existing data processing pipelines enables improved particle curation and the construction of quantitative conformational landscapes.

Protofilament-specific nanopatterns of tubulin post-translational modifications regulate the mechanics of ciliary beating

Controlling ciliary beating is essential for motility and signaling in eukaryotes. This process relies on the regulation of various axonemal proteins that assemble in stereotyped patterns onto individual microtubules of the ciliary structure. Additionally, each axonemal protein interacts exclusively with determined tubulin protofilaments of the neighboring microtubule to carry out its function. While it is known that tubulin post-translational modifications (PTMs) are important for proper ciliary motility, the mode and extent to which they contribute to these interactions remain poorly understood. Currently, the prevailing understanding is that PTMs can confer functional specialization at the level of individual microtubules. However, this paradigm falls short of explaining how the tubulin code can manage the complexity of the axonemal structure where functional interactions happen in defined patterns at the sub-microtubular scale. Here, we combine immuno-cryo-electron tomography (cryo-ET), expansion microscopy, and mutant analysis to show that, in motile cilia, tubulin glycylation and polyglutamylation form mutually exclusive protofilament-specific nanopatterns at a sub-microtubular scale. These nanopatterns are consistent with the distributions of axonemal dyneins and nexin-dynein regulatory complexes, respectively, and are indispensable for their regulation during ciliary beating. Our findings offer a new paradigm for understanding how different tubulin PTMs, such as glycylation, glutamylation, acetylation, tyrosination, and detyrosination, can coexist within the ciliary structure and specialize individual protofilaments for the regulation of diverse protein complexes. The identification of a ciliary tubulin nanocode by cryo-ET suggests the need for high-resolution studies to better understand the molecular role of PTMs in other cellular compartments beyond the cilium.

A robust normalized local filter to estimate compositional heterogeneity directly from cryo-EM maps

Cryo electron microscopy (cryo-EM) is used by biological research to visualize biomolecular complexes in 3D, but the heterogeneity of cryo-EM reconstructions is not easily estimated. Current processing paradigms nevertheless exert great effort to reduce flexibility and heterogeneity to improve the quality of the reconstruction. Clustering algorithms are typically employed to identify populations of data with reduced variability, but lack assessment of remaining heterogeneity. Here we develope a fast and simple algorithm based on spatial filtering to estimate the heterogeneity of a reconstruction. In the absence of flexibility, this estimate approximates macromolecular component occupancy. We show that our implementation can derive reasonable input parameters, that composition heterogeneity can be estimated based on contrast loss, and that the reconstruction can be modified accordingly to emulate altered constituent occupancy. This stands to benefit conventionally employed maximum-likelihood classification methods, whereas we here limit considerations to cryo-EM map interpretation, quantification, and particle-image signal subtraction.

Electron scattering properties of biological macromolecules and their use for cryo-EM map sharpening

Resolution-dependent loss of contrast in cryo-EM maps may obscure features at high resolution that are critical for map interpretation. Post-processing of cryo-EM maps can improve the interpretability by adjusting the resolution-dependence of structure factor amplitudes through map sharpening. Traditionally this has been done by rescaling the relative contribution of low and high-resolution frequencies globally. More recently, the realisation that molecular motion and heterogeneity cause non-uniformity of resolution throughout the map has inspired the development of techniques that optimise sharpening locally. We previously developed LocScale, a method that utilises the radial structure factor from a refined atomic model as a restraint for local map sharpening. While this method has proved beneficial for the interpretation of cryo-EM maps, the dependence on the availability of (partial) model information limits its general applicability. Here, we review the basic assumptions of resolution-dependent contrast loss in cryo-EM maps and propose a route towards a robust alternative for local map sharpening that utilises information on expected scattering properties of biological macromolecules, but requires no detailed knowledge of the underlying molecular structure. We examine remaining challenges for implementation and discuss possible applications.

Overview and applications of map and model validation tools in the CCP-EM software suite

Cryogenic electron microscopy (cryo-EM) has recently been established as a powerful technique for solving macromolecular structures. Although the best resolutions achievable are improving, a significant majority of data are still resolved at resolutions worse than 3 Å, where it is non-trivial to build or fit atomic models. The map reconstructions and atomic models derived from the maps are also prone to errors accumulated through the different stages of data processing. Here, we highlight the need to evaluate both model geometry and fit to data at different resolutions. Assessment of cryo-EM structures from SARS-CoV-2 highlights a bias towards optimising the model geometry to agree with the most common conformations, compared to the agreement with data. We present the CoVal web service which provides multiple validation metrics to reflect the quality of atomic models derived from cryo-EM data of structures from SARS-CoV-2. We demonstrate that further refinement can lead to improvement of the agreement with data without the loss of geometric quality. We also discuss the recent CCP-EM developments aimed at addressing some of the current shortcomings.

Quantitative mining of compositional heterogeneity in cryo-EM datasets of ribosome assembly intermediates

Single-particle cryoelectron microscopy (cryo-EM) offers a unique opportunity to characterize macromolecular structural heterogeneity by virtue of its ability to place distinct particle populations into different groups through computational classification. However, there is a dearth of tools for surveying the heterogeneity landscape, quantitatively analyzing heterogeneous particle populations after classification, deciding how many unique classes are represented by the data, and accurately cross-comparing reconstructions. Here, we develop a workflow that contains discovery and analysis modules to quantitatively mine cryo-EM data for sets of structures with maximal diversity. This workflow was applied to a dataset of E. coli 50S ribosome assembly intermediates, which are characterized by significant structural heterogeneity. We identified more detailed branchpoints in the assembly process and characterized the interactions of an assembly factor with immature intermediates. While the tools described here were developed for ribosome assembly, they should be broadly applicable to the analysis of other heterogeneous cryo-EM datasets.

Real space in cryo-EM: the future is local

Cryo-EM images have extremely low signal-to-noise levels because biological macromolecules are highly radiation-sensitive, requiring low-dose imaging, and because the molecules are poor in contrast. Confident recovery of the signal requires the averaging of many images, the iterative optimization of parameters and the introduction of much prior information. Poor parameter estimates, overfitting and variations in signal strength and resolution across the resulting reconstructions remain frequent issues. Because biological samples are real-space phenomena, exhibiting local variations, real-space measures can be both more reliable and more appropriate than Fourier-space measures. Real-space measures can be calculated separately over each differing region of an image or volume. Real-space filters can be applied according to the local need. Powerful prior information, not available in Fourier space, can be introduced in real space. Priors can be applied in real space in ways that Fourier space precludes. The treatment of biological phenomena remains highly dependent on spatial frequency, however, which would normally be handled in Fourier space. We believe that measures and filters based around real-space operations on extracted frequency bands, i.e. a series of band-pass filtered real-space volumes, and over real-space densities of striding (sequentially increasing or decreasing) resolution through Fourier space are the best way to address this and will perform better than global Fourier-space-based approaches. Future developments in image processing within the field are generally expected to be based on a mixture of both rationally designed and deep-learning approaches, and to incorporate novel prior information from developments such as AlphaFold. Regardless of approach, it is clear that `locality', through real-space measures, filters and processing, will become central to image processing.

Atomic model validation using the CCP-EM software suite

Recently, there has been a dramatic improvement in the quality and quantity of data derived using cryogenic electron microscopy (cryo-EM). This is also associated with a large increase in the number of atomic models built. Although the best resolutions that are achievable are improving, often the local resolution is variable, and a significant majority of data are still resolved at resolutions worse than 3 Å. Model building and refinement is often challenging at these resolutions, and hence atomic model validation becomes even more crucial to identify less reliable regions of the model. Here, a graphical user interface for atomic model validation, implemented in the CCP-EM software suite, is presented. It is aimed to develop this into a platform where users can access multiple complementary validation metrics that work across a range of resolutions and obtain a summary of evaluations. Based on the validation estimates from atomic models associated with cryo-EM structures from SARS-CoV-2, it was observed that models typically favor adopting the most common conformations over fitting the observations when compared with the model agreement with data. At low resolutions, the stereochemical quality may be favored over data fit, but care should be taken to ensure that the model agrees with the data in terms of resolvable features. It is demonstrated that further re-refinement can lead to improvement of the agreement with data without the loss of geometric quality. This also highlights the need for improved resolution-dependent weight optimization in model refinement and an effective test for overfitting that would help to guide the refinement process.

EMDA: A Python package for Electron Microscopy Data Analysis

An open-source Python library EMDA for cryo-EM map and model manipulation is presented with a specific focus on validation. The use of several functionalities in the library is presented through several examples. The utility of local correlation as a metric for identifying map-model differences and unmodeled regions in maps, and how it is used as a metric of map-model validation is demonstrated. The mapping of local correlation to individual atoms, and its use to draw insights on local signal variations are discussed. EMDA’s likelihood-based map overlay is demonstrated by carrying out a superposition of two domains in two related structures. The overlay is carried out first to bring both maps into the same coordinate frame and then to estimate the relative movement of domains. Finally, the map magnification refinement in EMDA is presented with an example to highlight the importance of adjusting the map magnification in structural comparison studies.

Structure of native HIV-1 cores and their interactions with IP6 and CypA

The viral capsid plays essential roles in HIV replication and is a major platform engaging host factors. To overcome challenges in study native capsid structure, we used the perfringolysin O to perforate the membrane of HIV-1 particles, thus allowing host proteins and small molecules to access the native capsid while improving cryo–electron microscopy image quality. Using cryo–electron tomography and subtomogram averaging, we determined the structures of native capsomers in the presence and absence of inositol hexakisphosphate (IP6) and cyclophilin A and constructed an all-atom model of a complete HIV-1 capsid. Our structures reveal two IP6 binding sites and modes of cyclophilin A interactions. Free energy calculations substantiate the two binding sites at R18 and K25 and further show a prohibitive energy barrier for IP6 to pass through the pentamer. Our results demonstrate that perfringolysin O perforation is a valuable tool for structural analyses of enveloped virus capsids and interactions with host cell factors.

Cryo-EM structures of PI3Kα reveal conformational changes during inhibition and activation

Phosphoinositide 3-kinases (PI3Ks) are of critical importance in cell signaling and can function as drivers of disease. Information on the PI3K structure is essential for an understanding of the function of these proteins and for the identification of specific and effective small-molecule inhibitors. Here we present a single-particle cryo-electron microscopy (cryo-EM) analysis of PI3Kα, the dimer consisting of the p110α catalytic subunit bound to the p85α regulatory subunit. We investigated three conformational states of PI3Kα: the unbound dimer, the dimer bound to the isoform-specific inhibitor BYL-719, and the dimer associated with an activating phosphopeptide. Each of these conformations reveals specific structural features that provide insights into conformation-associated functions.

Phosphoinositide 3-kinases (PI3Ks) are lipid kinases essential for growth and metabolism. Their aberrant activation is associated with many types of cancers. Here we used single-particle cryoelectron microscopy (cryo-EM) to determine three distinct conformations of full-length PI3Kα (p110α–p85α): the unliganded heterodimer PI3Kα, PI3Kα bound to the p110α-specific inhibitor BYL-719, and PI3Kα exposed to an activating phosphopeptide. The cryo-EM structures of unbound and of BYL-719–bound PI3Kα are in general accord with published crystal structures. Local deviations are presented and discussed. BYL-719 stabilizes the structure of PI3Kα, but three regions of low-resolution extra density remain and are provisionally assigned to the cSH2, BH, and SH3 domains of p85. One of the extra density regions is in contact with the kinase domain blocking access to the catalytic site. This conformational change indicates that the effects of BYL-719 on PI3Kα activity extend beyond competition with adenosine triphosphate (ATP). In unliganded PI3Kα, the DFG motif occurs in the “in” and “out” positions. In BYL-719–bound PI3Kα, only the DFG-in position, corresponding to the active conformation of the kinase, was observed. The phosphopeptide-bound structure of PI3Kα is composed of a stable core resolved at 3.8 Å. It contains all p110α domains except the adaptor-binding domain (ABD). The p85α domains, linked to the core through the ABD, are no longer resolved, implying that the phosphopeptide activates PI3Kα by fully releasing the niSH2 domain from binding to p110α. The structures presented here show the basal form of the full-length PI3Kα dimer and document conformational changes related to the activated and inhibited states.

Image Processing in Cryo-Electron Microscopy of Single Particles: The Power of Combining Methods

Cryo-electron microscopy has established as a mature structural biology technique to elucidate the three-dimensional structure of biological macromolecules. The Coulomb potential of the sample is imaged by an electron beam, and fast semi-conductor detectors produce movies of the sample under study. These movies have to be further processed by a whole pipeline of image-processing algorithms that produce the final structure of the macromolecule. In this chapter, we illustrate this whole processing pipeline putting in value the strength of "meta algorithms," which are the combination of several algorithms, each one with different mathematical rationale, in order to distinguish correctly from incorrectly estimated parameters. We show how this strategy leads to superior performance of the whole pipeline as well as more confident assessments about the reconstructed structures. The "meta algorithms" strategy is common to many fields and, in particular, it has provided excellent results in bioinformatics. We illustrate this combination using the workflow engine, Scipion.

Cryo-EM Map-Based Model Validation Using the False Discovery Rate Approach

Significant technological developments and increasing scientific interest in cryogenic electron microscopy (cryo-EM) has resulted in a rapid increase in the amount of data generated by these experiments and the derived atomic models. Robust measures for the validation of 3D reconstructions and atomic models are essential for appropriate interpretation of the data. The resolution of data and availability of software tools that work across a range of resolutions often limit the quality of derived models. Hence, the final atomic model is often incomplete or contains regions where atomic positions are less reliable or incorrectly built. Extensive manual pruning and local adjustments or rebuilding are usually required to address these issues. The presented research introduces a software tool for the validation of the backbone trace of atomic models built in the cryo-EM density maps. In this study, we use the false discovery rate analysis, which can be used to segregate molecular signals from the background. Each atomic position in the model can be associated with an FDR backbone validation score, which can be used to identify potential mistraced residues. We demonstrate that the proposed validation score is complementary to existing validation metrics and is useful especially in cases where the model is built in the maps having varying local resolution. We also discuss the application of the score for automated pruning of atomic models built ab-initio during the iterative model building process in Buccaneer. We have implemented this score in the CCP-EM software suite.

CryoET structures of immature HIV Gag reveal six-helix bundle

Gag is the HIV structural precursor protein which is cleaved by viral protease to produce mature infectious viruses. Gag is a polyprotein composed of MA (matrix), CA (capsid), SP1, NC (nucleocapsid), SP2 and p6 domains. SP1, together with the last eight residues of CA, have been hypothesized to form a six-helix bundle responsible for the higher-order multimerization of Gag necessary for HIV particle assembly. However, the structure of the complete six-helix bundle has been elusive. Here, we determined the structures of both Gag in vitro assemblies and Gag viral-like particles (VLPs) to 4.2 Å and 4.5 Å resolutions using cryo-electron tomography and subtomogram averaging by emClarity. A single amino acid mutation (T8I) in SP1 stabilizes the six-helix bundle, allowing to discern the entire CA-SP1 helix connecting to the NC domain. These structures provide a blueprint for future development of small molecule inhibitors that can lock SP1 in a stable helical conformation, interfere with virus maturation, and thus block HIV-1 infection.

Ccdc113/Ccdc96 complex, a novel regulator of ciliary beating that connects radial spoke 3 to dynein g and the nexin link

Ciliary beating requires the coordinated activity of numerous axonemal complexes. The protein composition and role of radial spokes (RS), nexin links (N-DRC) and dyneins (ODAs and IDAs) is well established. However, how information is transmitted from the central apparatus to the RS and across other ciliary structures remains unclear. Here, we identify a complex comprising the evolutionarily conserved proteins Ccdc96 and Ccdc113, positioned parallel to N-DRC and forming a connection between RS3, dynein g, and N-DRC. Although Ccdc96 and Ccdc113 can be transported to cilia independently, their stable docking and function requires the presence of both proteins. Deletion of either CCDC113 or CCDC96 alters cilia beating frequency, amplitude and waveform. We propose that the Ccdc113/Ccdc96 complex transmits signals from RS3 and N-DRC to dynein g and thus regulates its activity and the ciliary beat pattern.

Structural interpretation of cryo-EM image reconstructions

The productivity of single-particle cryo-EM as a structure determination method has rapidly increased as many novel biological structures are being elucidated. The ultimate result of the cryo-EM experiment is an atomic model that should faithfully represent the computed image reconstruction. Although the principal approach of atomic model building and refinement from maps resembles that of the X-ray crystallographic methods, there are important differences due to the unique properties resulting from the 3D image reconstructions. In this review, we discuss the practiced work-flow from the cryo-EM image reconstruction to the atomic model. We give an overview of (i) resolution determination methods in cryo-EM including local and directional resolution variation, (ii) cryo-EM map contrast optimization including complementary map types that can help in identifying ambiguous density features, (iii) atomic model building and (iv) refinement in various resolution regimes including (v) their validation and (vi) discuss differences between X-ray and cryo-EM maps. Based on the methods originally developed for X-ray crystallography, the path from 3D image reconstruction to atomic coordinates has become an integral and important part of the cryo-EM structure determination work-flow that routinely delivers atomic models.

A "Drug Sweeping" State of the TriABC Triclosan Efflux Pump from Pseudomonas aeruginosa

The structure of the TriABC inner membrane component of the triclosan/SDS-specific efflux pump from Pseudomonas aeruginosa was determined by cryoelectron microscopy to 4.5 Å resolution. The complete structure of the inner membrane transporter TriC of the resistance-nodulation-division (RND) superfamily was solved, including a partial structure of the fused periplasmic membrane fusion subunits, TriA and TriB. The substrate-free conformation of TriABC represents an intermediate step in efflux complex assembly before the engagement of the outer membrane channel. Structural analysis identified a tunnel network whose constriction impedes substrate efflux, indicating inhibition of TriABC in the unengaged state. Blind docking studies revealed binding to TriC at the same loci by substrates and bulkier non-substrates. Together with functional analyses, we propose that selective substrate translocation involves conformational gating at the tunnel narrowing that, together with conformational ordering of TriA and TriB, creates an engaged state capable of mediating substrate efflux.

A chemical interpretation of protein electron density maps in the worldwide protein data bank

High-quality three-dimensional structural data is of great value for the functional interpretation of biomacromolecules, especially proteins; however, structural quality varies greatly across the entries in the worldwide Protein Data Bank (wwPDB). Since 2008, the wwPDB has required the inclusion of structure factors with the deposition of x-ray crystallographic structures to support the independent evaluation of structures with respect to the underlying experimental data used to derive those structures. However, interpreting the discrepancies between the structural model and its underlying electron density data is difficult, since derived sigma-scaled electron density maps use arbitrary electron density units which are inconsistent between maps from different wwPDB entries. Therefore, we have developed a method that converts electron density values from sigma-scaled electron density maps into units of electrons. With this conversion, we have developed new methods that can evaluate specific regions of an x-ray crystallographic structure with respect to a physicochemical interpretation of its corresponding electron density map. We have systematically compared all deposited x-ray crystallographic protein models in the wwPDB with their underlying electron density maps, if available, and characterized the electron density in terms of expected numbers of electrons based on the structural model. The methods generated coherent evaluation metrics throughout all PDB entries with associated electron density data, which are consistent with visualization software that would normally be used for manual quality assessment. To our knowledge, this is the first attempt to derive units of electrons directly from electron density maps without the aid of the underlying structure factors. These new metrics are biochemically-informative and can be extremely useful for filtering out low-quality structural regions from inclusion into systematic analyses that span large numbers of PDB entries. Furthermore, these new metrics will improve the ability of non-crystallographers to evaluate regions of interest within PDB entries, since only the PDB structure and the associated electron density maps are needed. These new methods are available as a well-documented Python package on GitHub and the Python Package Index under a modified Clear BSD open source license.

Comparison of CryoEM and X-ray structures of dimethylformamidase

Dimethylformamidase (DMFase) catalyzes the hydrolysis of dimethylformamide, an industrial solvent, introduced into the environment by humans. Recently, we determined the structures of dimethylformamidase by electron cryo microscopy and X-ray crystallography revealing a tetrameric enzyme with a mononuclear iron at the active site. DMFase from Paracoccus sp. isolated from a waste water treatment plant around the city of Kanpur in India shows maximal activity at 54 °C and is halotolerant. The structures determined by both techniques are mostly identical and the largest difference is in a loop near the active site. This loop could play a role in co-operativity between the monomers. A number of non-protein densities are observed in the EM map, which are modelled as water molecules. Comparison of the structures determined by the two methods reveals conserved water molecules that could play a structural role. The higher stability, unusual active site and negligible activity at low temperature makes this a very good model to study enzyme mechanism by cryoEM.

Comparing Cryo-EM Reconstructions and Validating Atomic Model Fit Using Difference Maps

Cryogenic electron microscopy (cryo-EM) is a powerful technique for determining structures of multiple conformational or compositional states of macromolecular assemblies involved in cellular processes. Recent technological developments have led to a leap in the resolution of many cryo-EM data sets, making atomic model building more common for data interpretation. We present a method for calculating differences between two cryo-EM maps or a map and a fitted atomic model. The proposed approach works by scaling the maps using amplitude matching in resolution shells. To account for variability in local resolution of cryo-EM data, we include a procedure for local amplitude scaling that enables appropriate scaling of local map contrast. The approach is implemented as a user-friendly tool in the CCP-EM software package. To obtain clean and interpretable differences, we propose a protocol involving steps to process the input maps and output differences. We demonstrate the utility of the method for identifying conformational and compositional differences including ligands. We also highlight the use of difference maps for evaluating atomic model fit in cryo-EM maps.

Confidence maps: statistical inference of cryo-EM maps

Confidence maps provide complementary information for interpreting cryo-EM densities as they indicate statistical significance with respect to background noise. They can be thresholded by specifying the expected false-discovery rate (FDR), and the displayed volume shows the parts of the map that have the corresponding level of significance. Here, the basic statistical concepts of confidence maps are reviewed and practical guidance is provided for their interpretation and usage inside the CCP-EM suite. Limitations of the approach are discussed and extensions towards other error criteria such as the family-wise error rate are presented. The observed map features can be rendered at a common isosurface threshold, which is particularly beneficial for the interpretation of weak and noisy densities. In the current article, a practical guide is provided to the recommended usage of confidence maps.

Current developments in Coot for macromolecular model building of Electron Cryo-microscopy and Crystallographic Data

Coot is a tool widely used for model building, refinement, and validation of macromolecular structures. It has been extensively used for crystallography and, more recently, improvements have been introduced to aid in cryo‐EM model building and refinement, as cryo‐EM structures with resolution ranging 2.5–4 A are now routinely available. Model building into these maps can be time‐consuming and requires experience in both biochemistry and building into low‐resolution maps. To simplify and expedite the model building task, and minimize the needed expertise, new tools are being added in Coot. Some examples include morphing, Geman‐McClure restraints, full‐chain refinement, and Fourier‐model based residue‐type‐specific Ramachandran restraints. Here, we present the current state‐of‐the‐art in Coot usage.

Need for Cross-Validation of Single Particle Cryo-EM

Cross-validation is used to determine the validity of a model on unseen data by assessing if the model is overfitted to noise. It is widely used in many fields, from artificial intelligence to structural biology in X-ray crystallography and nuclear magnetic resonance. Although there are concerns of map overfitting in cryo-electron microscopy (cryo-EM), cross-validation is rarely used. The problem is that establishing a performance metric of the maps over unseen data (given by 2D-projection images) is difficult due to the low signal-to-noise ratios in the individual particles. Here, I present recent advances for cryo-EM map reconstruction. I highlight that the gold-standard procedure can fail to detect map overfitting in certain cases, showing the necessity of assessing the map quality on unbiased data. Finally, I describe the challenges and advantages of developing a robust cross-validation methodology for cryo-EM.

Cryo-EM structure of the respiratory syncytial virus RNA polymerase

The respiratory syncytial virus (RSV) RNA polymerase, constituted of a 250 kDa large (L) protein and tetrameric phosphoprotein (P), catalyzes three distinct enzymatic activities — nucleotide polymerization, cap addition, and cap methylation. How RSV L and P coordinate these activities is poorly understood. Here, we present a 3.67 Å cryo-EM structure of the RSV polymerase (L:P) complex. The structure reveals that the RNA dependent RNA polymerase (RdRp) and capping (Cap) domains of L interact with the oligomerization domain (P_OD) and C-terminal domain (P_CTD) of a tetramer of P. The density of the methyltransferase (MT) domain of L and the N-terminal domain of P (P_NTD) is missing. Further analysis and comparison with other RNA polymerases at different stages suggest the structure we obtained is likely to be at an elongation-compatible stage. Together, these data provide enriched insights into the interrelationship, the inhibitors, and the evolutionary implications of the RSV polymerase.

Respiratory syncytial virus (RSV) is a pathogenic non-segmented negative-sense RNA virus and active RSV polymerase is composed of a 250 kDa large (L) protein and tetrameric phosphoprotein (P). Here, the authors present the 3.67 Å cryo-EM structure of the RSV polymerase (L:P) complex.

Elucidation of the viral disassembly switch of tobacco mosaic virus

Stable capsid structures of viruses protect viral RNA while they also require controlled disassembly for releasing the viral genome in the host cell. A detailed understanding of viral disassembly processes and the involved structural switches is still lacking. This process has been extensively studied using tobacco mosaic virus (TMV), and carboxylate interactions are assumed to play a critical part in this process. Here, we present two cryo‐EM structures of the helical TMV assembly at 2.0 and 1.9 Å resolution in conditions of high Ca²⁺ concentration at low pH and in water. Based on our atomic models, we identify the conformational details of the disassembly switch mechanism: In high Ca²⁺/acidic pH environment, the virion is stabilized between neighboring subunits through carboxyl groups E95 and E97 in close proximity to a Ca²⁺ binding site that is shared between two subunits. Upon increase in pH and lower Ca²⁺ levels, mutual repulsion of the E95/E97 pair and Ca²⁺ removal destabilize the network of interactions between adjacent subunits at lower radius and release the switch for viral disassembly.

Interpretation of medium resolution cryoEM maps of multi-protein complexes

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CryoEM maps at medium (3.5–6 Å) resolution can be challenging to interpret.

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Integration of multiple methods can inform cryoEM studies.

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Mass spectrometry and biochemistry facilitate map interpretation and model building.

Electron cryo-microscopy (cryoEM) is used to determine structures of biological molecules, including multi-protein complexes. Maps at better than 3.0 Å resolution are relatively straightforward to interpret since atomic models of proteins and nucleic acids can be built directly. Still, these resolutions are often difficult to achieve, and map quality frequently varies within a structure. This results in data that are challenging to interpret, especially when crystal structures or suitable homology models are not available. Recent advances in mass spectrometry techniques, computational methods and model building tools facilitate subunit/domain fitting into maps, elucidation of protein contacts, and de novo generation of atomic models. Here, we review techniques for map interpretation and provide examples from recent studies of multi-protein complexes.

Challenges and opportunities in cryo-EM single-particle analysis

Cryogenic electron microscopy (cryo-EM) enables structure determination of macromolecular objects and their assemblies. Although the techniques have been developing for nearly four decades, they have gained widespread attention in recent years due to technical advances on numerous fronts, enabling traditional microscopists to break into the world of molecular structural biology. Many samples can now be routinely analyzed at near-atomic resolution using standard imaging and image analysis techniques. However, numerous challenges to conventional workflows remain, and continued technical advances open entirely novel opportunities for discovery and exploration. Here, I will review some of the main methods surrounding cryo-EM with an emphasis specifically on single-particle analysis, and I will highlight challenges, open questions, and opportunities for methodology development.

Interpreting the cryo-EM map

Beckers et al. [IUCrJ (2019), 6, 18-33] propose a general approach to visualization of cryo-EM maps. Their method overcomes some of the challenges inherent in the conventional approach for depicting maps using a single isosurface threshold.